Sublinear time estimation of degree distribution moments: The arboricity connection

Talya Eden, Dana Ron, C. Seshadhri

Research output: Contribution to journalArticlepeer-review

Abstract

We revisit the classic problem of estimating the moments of the degree distribution of an undirected simple graph. Consider an undirected simple graph G = (V, E) with n (nonisolated) vertices, and define (for s > 0) Ms = Σu∈V ds v. Our aim is to estimate Ms within a multiplicative error of (1 + (for a given approximation parameter ϵ > 0) in sublinear time. We consider the sparse-graph model that allows access to uniform random vertices, queries for the degree of any vertex, and queries for a neighbor of any vertex. For the case of s = 1 (the average degree), O(√n) queries suffice for any constant ϵ[U. Feige, SIAM J. Comput., 35(2006), pp. 964-984], [O. Goldreich and D. Ron, Random Structures Algorithms, 32(2008), pp. 473-493]. (We use the O notation to suppress dependencies in log n and 1/ϵ.) Gonen, Ron, and Shavitt [SIAM J. Discrete Math., 25(2011), pp. 1365-1411] extended this result to all integral s > 0 by designing an algorithm that performs O(n1-1/(s+1)) queries. (Strictly speaking, their algorithm approximates the number of star-subgraphs of a given size, but a slight modification gives an algorithm for moments.) We design a new, significantly simpler algorithm for this problem. In the worst case, it exactly matches the bounds of Gonen, Ron, and Shavitt and has a much simpler proof. More importantly, the running time of this algorithm is connected to the arboricity of G. This is (essentially) the maximum density of an induced subgraph. For the family of graphs with arboricity at most it has a query complexity of O(n·α1/s/M1/s s + min { m/M1/s s, m·ns-1/Ms}) which is always upper bounded by O(nα/M1/s s). Thus, for the class of constant-arboricity graphs (which includes, among others, all minor-closed families and preferential attachment graphs), we can estimate the average degree in O(1) queries, and we can estimate the variance of the degree distribution in O(√n) queries. This is a major improvement over the previous worst-case bounds.

Original languageEnglish
Pages (from-to)2267-2285
Number of pages19
JournalSIAM Journal on Discrete Mathematics
Volume33
Issue number4
DOIs
StatePublished - 2019

Keywords

  • Approximation algorithms
  • Arboricity
  • Degeneracy
  • Degree distribution
  • Degree moments
  • Sublinear time algorithms

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